CN113590823A - Contract approval method and device, storage medium and electronic equipment - Google Patents
Contract approval method and device, storage medium and electronic equipment Download PDFInfo
- Publication number
- CN113590823A CN113590823A CN202110872691.4A CN202110872691A CN113590823A CN 113590823 A CN113590823 A CN 113590823A CN 202110872691 A CN202110872691 A CN 202110872691A CN 113590823 A CN113590823 A CN 113590823A
- Authority
- CN
- China
- Prior art keywords
- contract
- target
- keywords
- approval
- keyword
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000013528 artificial neural network Methods 0.000 claims abstract description 24
- 238000012216 screening Methods 0.000 claims abstract description 23
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 22
- 238000003064 k means clustering Methods 0.000 claims abstract description 22
- 238000000605 extraction Methods 0.000 claims abstract description 7
- 238000012549 training Methods 0.000 claims description 21
- 238000013507 mapping Methods 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 9
- 238000001914 filtration Methods 0.000 claims 1
- 230000008569 process Effects 0.000 description 15
- 238000012552 review Methods 0.000 description 6
- 238000003062 neural network model Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 3
- 238000007619 statistical method Methods 0.000 description 3
- 101100481876 Danio rerio pbk gene Proteins 0.000 description 1
- 101100481878 Mus musculus Pbk gene Proteins 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000003058 natural language processing Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
- G06F16/35—Clustering; Classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/18—Legal services
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Health & Medical Sciences (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Technology Law (AREA)
- Life Sciences & Earth Sciences (AREA)
- Probability & Statistics with Applications (AREA)
- Evolutionary Biology (AREA)
- Economics (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Marketing (AREA)
- Primary Health Care (AREA)
- Strategic Management (AREA)
- General Business, Economics & Management (AREA)
- Databases & Information Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The application discloses a contract approval method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a target contract to be examined and approved; extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords; screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords; determining a contract type for the target contract; and examining and approving the keywords at least based on an examination and approval rule corresponding to the contract type to obtain an examination and approval result of the target contract. According to the method and the device, the keyword extraction is carried out on the contract file by utilizing the target neural network language model to obtain a plurality of keywords, and then the keywords are examined and approved by utilizing the preset examination and approval rule corresponding to the contract type, so that the examination and approval speed of the contract can be improved, and the problem that a large amount of labor and time are consumed for contract examination and approval is solved.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a contract approval method, a contract approval device, a contract approval storage medium and electronic equipment.
Background
In the process of signing a contract, a contract document is usually approved manually by an enterprise, or only the contract document is input online, the follow-up examination and approval of the contract still needs to be completed manually, a large amount of manpower and time are consumed, the examination and approval efficiency is low, and some problems which are difficult to find are missed sometimes in manual examination and approval, such as cross-level signing and the like, so that the problem that the examination and approval of the contract document are not accurate enough is caused. In addition, because the contract is managed manually, after the contract is signed, the data in the contract cannot be subjected to statistical analysis, for example, the amount of the contract cannot be counted, an enterprise with poor performance cannot be counted, and risks cannot be avoided for the enterprise.
Disclosure of Invention
In view of this, the present application provides a contract approval method, a contract approval apparatus, a storage medium, and an electronic device, and mainly aims to solve the problems that the current manual contract approval is not accurate enough and the approval efficiency is low.
In order to solve the above problem, the present application provides a contract approval method, including:
acquiring a target contract to be examined and approved;
extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
determining a contract type for the target contract;
and examining and approving each target keyword at least based on an examination and approval rule corresponding to the contract type to obtain an examination and approval result of the target contract.
Optionally, the method for training the target neural network language model includes:
obtaining a plurality of corpus samples of contract types;
acquiring keywords corresponding to each of the speech samples to obtain a keyword set;
and training a neural network language model based on the corpus sample and the keyword set to obtain the target neural network language model.
Optionally, the screening the keywords based on the K-means clustering algorithm specifically includes:
calculating the distance between each keyword and a clustering center based on a K-means clustering algorithm;
and screening the keywords based on the distance between the keywords and the clustering center to obtain the target keywords.
Optionally, after each target keyword is approved, the method further includes:
displaying the keywords which are not approved according to a preset display rule according to a preset display mode so as to prompt for selection, wherein after the target keywords are obtained through screening, the method further comprises the following steps:
acquiring position information of the target keyword in the target contract;
establishing a mapping relation between each target keyword and the position information to obtain a mapping relation table;
under the condition that the target keyword is not approved, searching the mapping relation table based on the target keyword to obtain the position information corresponding to the target keyword,
and displaying the keywords at the corresponding positions in the target contract according to a preset display mode based on the position information.
Optionally, before approving each of the target keywords, the method further includes:
determining the keyword type of the missing keywords to be acquired based on the preset keyword type and the keyword type of the target keywords;
and acquiring the missing keywords based on the keyword types of the missing keywords, and examining and approving the target keywords and the missing keywords based on an examination and approval rule corresponding to the contract type.
Optionally, after obtaining the target contract, the method further includes:
determining a target storage location based on the format of the target contract for storage of the target contract.
In order to solve the above problem, the present application provides a contract approval apparatus, including:
the acquisition module is used for acquiring a target contract to be examined and approved;
the extraction module is used for extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
the screening module is used for screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
a determining module for determining a contract type of the target contract;
an approval module for approving each target keyword at least based on an approval rule corresponding to the contract type to obtain an approval result of the target contract
In order to solve the above problem, the present application provides an electronic device, which at least includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the contract approval method according to any one of the above methods when executing the computer program on the memory.
To solve the above problem, the present application provides a storage medium storing a computer program, which when executed by a processor implements the steps of the contract approval method according to any one of the above.
According to the contract approval method, the contract approval device, the storage medium and the electronic equipment, a plurality of keywords are obtained by extracting keywords from a contract file through a target neural network language model, the keywords are screened through a K-means clustering algorithm to obtain a plurality of target keywords, and then the keywords are approved through a preset approval rule corresponding to the contract type, so that the approval rate of the contract can be improved, and the problem that the contract approval consumes a large amount of manpower and time is solved.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a flow chart of a contract approval method according to an embodiment of the present application;
FIG. 2 is a flow chart of a contract approval process according to yet another embodiment of the present application;
FIG. 3 is a flowchart of a contract administration according to an embodiment of the present application;
FIG. 4 is a flowchart of obtaining a target key in an embodiment of the present application;
fig. 5 is a block diagram of a contract approval apparatus according to another embodiment of the present application.
Detailed Description
Various aspects and features of the present application are described herein with reference to the drawings.
It will be understood that various modifications may be made to the embodiments of the present application. Accordingly, the foregoing description should not be construed as limiting, but merely as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the application.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the application and, together with a general description of the application given above and the detailed description of the embodiments given below, serve to explain the principles of the application.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It is also to be understood that although the present application has been described with reference to some specific examples, those skilled in the art are able to ascertain many other equivalents to the practice of the present application.
The above and other aspects, features and advantages of the present application will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present application are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely exemplary of the application, which can be embodied in various forms. Well-known and/or repeated functions and constructions are not described in detail to avoid obscuring the application of unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the application.
The embodiment of the application provides a contract approval method, as shown in fig. 1, which includes the following steps:
step S101, acquiring a target contract to be examined and approved;
in the specific implementation process of the step, the target contract to be examined and approved can be obtained from a preset platform system, and the contract attachment manually input or manually uploaded can be received as the target contract. The format of the target contract may be a text format or a picture format, and may be, for example: word format, pdf format, text format, jpg format, png format, etc. In this step, when the target contract is in the non-word format, format conversion can be performed on the target contract in the non-word format, so as to obtain a contract file in the word format.
Step S102, extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
in the specific implementation process of this step, a natural language processing model may be specifically used to extract information of the target contract to obtain a plurality of corpora, for example, the same document is divided according to document paragraphs, punctuations, etc. to obtain individual sentences as a prediction, and then a target neural network language model obtained by pre-training is used to extract keywords from each corpus/sentence, thereby obtaining a plurality of keywords.
S103, screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
step S104, determining the contract type of the target contract;
in this step, the contract type may specifically include any one of the following: a headquarter contract type and a non-headquarter contract type. Of course, the contract type can be refined according to the actual need, for example, the contract type can also be refined into a lease type contract, a buying and selling contract, a technical contract, a construction project contract, a contract, and the like.
Step S105, examining and approving each target keyword at least based on the examination and approval rule corresponding to the contract type, and obtaining the examination and approval result of the target contract.
In the specific implementation process of the step, the corresponding relation between the contract type and the approval rule can be established in advance, then after the contract type is determined, the corresponding approval rule can be rapidly determined by searching the corresponding relation, and the approval rule is utilized to examine and verify the target keyword so as to obtain the approval result.
According to the contract approval method, a plurality of keywords are obtained by extracting the keywords from the contract file through the target neural network language model, the keywords are screened through the K-means clustering algorithm to obtain a plurality of target keywords, and then the keywords are approved through the preset approval rule corresponding to the contract type, so that the contract approval rate can be improved, and the problem that a large amount of labor and time are consumed in contract approval is solved.
Another embodiment of the present application provides a contract approval method, as shown in fig. 2, including the following steps:
step S201, obtaining a plurality of corpus samples of contract types; acquiring keywords corresponding to each of the speech samples to obtain a keyword set; training a neural network language model based on the corpus sample and the keyword set to obtain the target neural network language model;
in this step, corpus samples, i.e., sentences, for model training can be obtained from a plurality of contract documents; keywords in the prospective sample are then obtained. The model training is specifically to use the pre-training model embedded based on shallow words by using NNLM
Performing training, wherein wtRepresenting the t-th word in the sequence of words,
representing the subsequence from the 1 st key to the t key words, P representing the probability, i.e. inputting the subsequence of the 1 st word to the t-1 st word to predict and obtain the key word wtF denotes the probability distribution of the calculation condition. And predicting through the training model to finally obtain candidate keywords, converting the candidate keywords into word vector files, and further screening the candidate keywords. For example, sentences including keywords, underwriters, parties A, parties B, contract amount and the like in the contract are extracted, the words are segmented and words and sentences without practical significance are removed, for example, "the contract underwriter is China's safety property insurance Limited company, the insurance period is from xxxx to xxxx, and the underwriting amount is xxxx (RMB)", and the sentence is finally segmented into "the underwriter is China's safety property insurance Limited company" and "the underwriting amount is xxxx. Specifically, the model principle is as follows: 1. inputting a model: first, a series of text sequences (w) with the length of n are collected from a corpust,wt-1,...,wt-n+1) Then, a training set D is formed, the corpus is a text corpus collected in the contract field, and the corpus is used as training data, and meanwhile, a corresponding keyword set is obtained and used as a dictionary. Model training is then performed based on the training data and the dictionary. First, a single sentence sequence is calculated, which can also be said to be a single sample, such as: w is a1…wtWherein wtE.v, V is the set of all words (i.e. the lexicon) Vi represents the lexiconThe ith word in (1). 2. Model parameters: the goal of NNLM is to train a model that means the probability that the nth word is predicted by the (t-1) words that precede it when a segment sequence is given.
Wherein, wtRepresenting the t-th word in the sequence of words,
representing a subsequence consisting of the 1 st word through the t-th word. The model needs to satisfy the following two constraints:
the first limiting condition is as follows: f (w)t,wt-1,...,wt-n+2,wt-n+1)>And 0, the limiting condition indicates that each probability value obtained by the neural network model is greater than 0.
The second limiting condition is as follows:
the limitation condition represents: the resulting output of the neural network model is to predict what the next, i.e., tth word, is for every t-1 word input. The actual output of the model is thus a vector, each component of which in turn corresponds to the probability that the next word is a word in the dictionary. There must be one of the probability values of the | v | dimension that is the largest probability, while the others are smaller. In the step, the final target neural network model can be obtained by performing model training in the above manner, and then keyword extraction can be performed by using the neural network model.
Step S202, obtaining a target contract to be examined and approved;
step S203, extracting keywords from the target contract based on the target neural network language model to obtain a plurality of keywords;
step S204, calculating the distance between each keyword and a clustering center based on a K-means clustering algorithm; screening each keyword based on the distance between each keyword and a clustering center to obtain a target keyword;
in this step, in order to make the obtained keywords more reasonable and accurate, each keyword may be further screened to obtain the target keyword. Firstly, an initial clustering center is randomly selected, word vector files are used for converting all keywords to obtain word vector representations of all the keywords, then a K-means clustering algorithm, namely a K mean clustering algorithm, is used for calculating the distance between each keyword and the initial clustering center, then all the keywords are classified according to the clustering between each keyword and the clustering center to obtain a plurality of clustering clusters, then an average value is calculated based on all the clustering clusters to serve as a new clustering center, then the distance between each keyword and the clustering center is calculated, and finally the keywords are screened according to the distance between each keyword and the clustering center to obtain target keywords. For example, the keywords obtained by the screening include "contract", "amount", "my a limited liability company", "other B limited liability company", "effective date 2020 year 1 month 1 day", "due date 2021 year 1 month 1 day", "total amount of contract 100 ten thousand rmb", "signature date 2020 year 12 month 1 day", "seal date 2020 year 12 month 1 day", and "seal object: a, and the like, then determining the clustering centers as "contract" and "amount", respectively calculating the clustering of each keyword and the two clustering centers, obtaining all keywords and the desired clustering in the clustering after a plurality of iterations, and taking Topk (top) closest to the clustering center as a finally selected target keyword, for example, finally obtaining the keywords comprises: "my party a limited liability company", "other party B limited liability company", "effective date 2020 year 1 month 1 day", "due date 2021 year 1 month 1 day", "total amount of contract 100 ten thousand renminbi", and "object of stamp use: a ".
Step S205, determining the contract type of the target contract;
in this step, the contract type may specifically include any one of the following: a headquarter contract type and a non-headquarter contract type. The contract type can be refined according to actual needs, for example, the contract type can also be a lease type contract, a buying and selling contract, a technical contract, a construction project contract, a contract, and the like.
Step S206, each target keyword is approved based on the approval rule corresponding to the contract type, and the approval result of the target contract is obtained.
Taking the example that the contract types include a headquarter contract type and a non-headquarter contract type in this step, the approval rules may include a first approval rule corresponding to the headquarter contract type and a second approval rule corresponding to the non-headquarter contract type.
Specifically, the first approval rule may include any one or more of the following: verifying the signing main body and judging whether the signing main body is a headquarter name or not; verifying the signed amount, and judging whether the signed amount is smaller than a first preset value; the method comprises the steps of checking a printing object and judging whether the printing object is a preset first printing object; and auditing the other party information, and judging whether the credit corresponding to the other party information is good or not.
The second approval rule may include any one or more of the following: verifying the signing main body, and judging whether the signing main body is a subordinate agency name; verifying the signed amount, and judging whether the signed amount is smaller than a second preset value; the printing object is checked, and whether the printing object is a preset second printing object is judged; and auditing the other party information, and judging whether the credit corresponding to the other party information is good or not.
For example, when the contract type is determined to be a non-headquarter contract type, and the approval rule is determined to be a second approval rule, and the following target keywords are obtained: "my party a limited liability company", "other party B limited liability company", "effective date 2020 year 1 month 1 day", "due date 2021 year 1 month 1 day", "total amount of contract 100 ten thousand renminbi", and "object of stamp use: and A', the target key can be approved, for example, whether the approval "My A company with limited responsibility" is the name of the subordinate organization, and if not, the signing subject is determined to be wrong and the signing subject fails to approve. And (3) examining whether the ' total amount of the contract of 100 million RMB ' is smaller than a second preset value, for example, examining whether the ' total amount of the contract of 100 million RMB ' is smaller than 500 million, and determining that the total amount of the contract is approved if the ' total amount of the contract of 500 million is smaller. Examine "print object: if the first print object is a preset second print object, the first print object passes the approval of the second print object. And examining and approving the credit corresponding to the information of the other party 'other party B finite responsibility company', judging whether the credit of the 'other party B finite responsibility company' is good, and if so, examining and approving the credit of the other party.
In the specific implementation process of the embodiment, the reputation of the other party enterprise/unit that has signed the contract can be determined according to the performance condition of the historical contract, and then the corresponding relationship between the other party enterprise/unit and the reputation is established, so that the reputation condition of the other party can be quickly obtained by searching the corresponding relationship. Specifically, the reputation may be ranked, for example, the reputation of a company/unit that has been paid but has not been paid due may be set to be poor, the reputation of a company/unit that has been paid but has been paid due may be set to be general, and the reputation of a company/unit that has been paid according to contract rules may be set to be good. Therefore, when reputation approval is carried out, different approval results can be obtained according to different reputation levels, for example, approval of an enterprise/unit with a poor reputation level is failed, approval of an enterprise/unit with a general reputation level is passed, risk prompt is carried out according to a preset prompt mode, and approval of an enterprise/unit with a good reputation level is passed.
In a specific implementation process of the embodiment, after the approval results of the keys are obtained, the keywords that do not pass the approval may be highlighted according to a predetermined display mode for prompting. For example, the failed keywords are displayed according to the preset font color, so that the user can check or modify the related content of the contract document in a targeted manner, and the examination and approval efficiency of the contract document is improved.
In the embodiment, in order to enable a user to quickly find out a specific position in a contract file, where the specific position of related content needs to be further confirmed and modified, that is, to determine a specific position of a keyword which has not been approved in the contract file, after extracting and obtaining each keyword, the position information of each keyword in a target contract can be further obtained, so that when each keyword is screened and obtained to obtain a target keyword, the position information of each target keyword in the target contract can be directly obtained; establishing a mapping relation between each target keyword and the position information to obtain a mapping relation table; and under the condition that the examination and approval of the target keywords are not passed, searching the mapping relation table based on the target keywords to obtain position information corresponding to the target keywords, and displaying the keywords at the corresponding positions in the target contract based on the position information and according to a preset display mode. For example, when the total amount of the contract 600 ten thousand RMB is approved to be not less than the predetermined 500 ten thousand RMB, it may be determined that the total amount of the contract is not approved, and at this time, the position information of the total amount of the contract 600 ten thousand RMB in the target contract may be found by looking up the mapping relation table, for example, it is determined that the total amount of the contract 600 ten thousand RMB is located in the 2 nd row in the 3 rd page of the target contract, and the corresponding position of the target contract may be directly highlighted, for example, the text in the 2 nd row in the 3 rd page is displayed according to a predetermined text color, or the background in the 2 nd row in the 3 rd page is displayed according to a predetermined shading color, so as to quickly find the position of the document to be modified.
In the specific implementation process of the embodiment, in order to make the final approval result more accurate, the missing keywords can be obtained manually. Specifically, the keyword type of the missing keyword to be acquired is determined based on a preset keyword type and the keyword type of the target keyword; and then acquiring the missing keywords based on the keyword types of the missing keywords, and examining and approving the target keywords and the missing keywords based on an examination and approval rule corresponding to the contract type. That is, the type of the keyword to be examined and approved, which needs to be obtained, is preset, for example, the keyword type may include a contract amount, a name of my party, a name of other party, and the like, and when the keyword type of the obtained target keyword includes only the contract amount, the name of my party, and is less than a predetermined keyword type, it is described that the obtained target keyword is missing, so that the keyword type of the missing keyword to be obtained may be determined as the name of other party, and then the missing keyword is further obtained. For example, after the target keyword is acquired, the target keyword may be filled in the approval template, and when the type of the acquired target keyword is less than a predetermined keyword type, that is, when an unfilled position exists in the approval template, it is indicated that keyword extraction is missing. Therefore, missing keywords can be manually acquired according to the keyword types of the missing keywords, and then the acquired missing keywords are filled in the corresponding positions of the examination and approval template, so that each keyword in the examination and approval template can be examined and approved according to the examination and approval rules, and examination and approval results can be obtained. In the embodiment, the missing keywords are obtained manually, so that a foundation is laid for follow-up examination and approval of the keywords, and a guarantee is provided for follow-up fast examination and approval of the target contract based on the keywords. In this embodiment, in order to make the final approval result more accurate, after the approval result is obtained, review may be performed manually. By only reviewing the approved contract, the workload of workers can be reduced, and the approval rate of the contract is improved while the accuracy of the contract file is ensured.
In a specific implementation process of this embodiment, after the target contract is obtained, a storage location of the target contract may be further determined based on a format of the target contract, so as to store the target contract. In this embodiment, contract files of different format types are provided with different storage locations, and a corresponding relationship between the format and the storage locations is established, for example, a word format contract file may be provided with the storage location a, and a PDF format contract file may be provided with the storage location B, so that target contracts of the same format may be stored at the same location, and thus the target contracts are backed up, and problems that the target contracts are lost due to errors and the contracts need to be uploaded again are avoided. And when the target contract is checked in subsequent manual work, the storage position of the contract can be quickly determined according to the format of the contract, so that the target contract can be quickly found.
In this embodiment, after the target contract is approved, the target contract may be sealed and signed according to the print-using object in the extracted target keyword. After the printing is finished, contracts can be classified and classified, and the contracts are uniformly stored in a first preset position, for example, the contracts can be classified and stored according to the contract types and the amount of money related to the contracts.
For further explanation of the above embodiment, the contract approval process is described in detail below with reference to fig. 3 and 4. Step S1 is performed after contract entry begins to select the type of target contract. Then step S2 is executed to upload the contract attachment. Specifically, the method includes the steps of identifying contract attachments to obtain a text file, performing AI parsing on a contract of the text file type by using a target neural network model, namely executing step S3 to perform AI parsing on the content of the contract to obtain a plurality of keywords, then executing step S4 to screen the keywords to obtain a plurality of target keywords, wherein a specific process of specifically obtaining the target keywords can be as shown in fig. 4, firstly performing model training in step S41, then processing the target contract by using a word vector file, obtaining a preprocessed text in step S42, then performing keyword extraction on the preprocessed text by using the model obtained by training, obtaining a plurality of candidate keywords in step S43, converting the candidate keywords by using the word vector file, obtaining word vector representations of the candidate keywords in step S44, obtaining a clustering center by using a K-MEANS clustering method in step S45, calculating a distance between each candidate keyword and the clustering center, s46, the candidate keywords are ranked according to the Manhattan distance between the candidate keywords and the cluster center, and S47 selects Top-K as the last target keyword, namely selects the candidate keywords with the distance less than the preset value as the last target keyword. If the parsing is successful and the screening is completed, step S5 is executed to automatically fill contract elements, that is, to fill the obtained target keywords to each target position in the predetermined approval, and if the parsing is not successful or all the target keywords are not obtained, step S6 is executed to automatically fill part of the keywords and to manually fill the rest. And then, the step S7 of submitting the contract and performing rule verification, that is, submitting the approval targets filled with the target keywords to a corresponding auditing module, so as to approve each target keyword in the approval targets by using the approval regulation corresponding to the target contract type. And if the examination and approval fails, returning, and pushing the contract flow to a node for uploading the contract attachment. If the approval is successful, step S8 is executed to enter into a review, for example, the contract document that is qualified for approval may be reviewed manually. And if the review fails, pushing the flow of the contract to a node for uploading the contract attachment. If the review is passed, step S9 is executed to print the contract, step S10 is executed to archive the contract, step S11 is executed to perform, and step S12 is executed to complete the statistical analysis of the data. In the implementation process, the ongoing contracts can be further managed uniformly in the performance process, for example, the contracts are classified and stored according to the contract types and the amount of money involved in the contracts. And after the fulfillment is finished, further performing statistical analysis on the data to determine an enterprise with poor fulfillment condition so as to perform risk reminding in the subsequent cooperation with the enterprise.
In order to solve the above technical problem, another embodiment of the present application provides a contract approval apparatus, as shown in fig. 5, including:
the acquisition module 1 is used for acquiring a target contract to be examined and approved;
the extraction module 2 is used for extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
the screening module 3 is used for screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
a determining module 4, configured to determine a contract type of the target contract;
an approval module 5, configured to approve each keyword at least based on an approval rule corresponding to the contract type, to obtain an approval result of the target contract
The contract approval apparatus in this embodiment further includes a model training module, and the model training module is configured to: acquiring a plurality of corpus samples of contract types; acquiring keywords corresponding to each of the speech samples to obtain a keyword set; and training a neural network language model based on the corpus sample and the keyword set to obtain the target neural network language model.
The screening module in this embodiment is specifically configured to: calculating the distance between each keyword and a clustering center based on a K-means clustering algorithm; and screening the keywords based on the distance between the keywords and the clustering center to obtain target keywords, and examining and approving the target keywords based on an examination and approval rule corresponding to the contract type.
In this embodiment, the contract type includes any one of the following: a headquarter contract type and a non-headquarter contract type; the approval rules comprise a first approval rule corresponding to the headquarter contract type and a second approval rule corresponding to the non-headquarter contract type. The first approval rule comprises any one or more of the following: verifying the signing main body and judging whether the signing main body is a headquarter name or not; verifying the signed amount, and judging whether the signed amount is smaller than a first preset value; the method comprises the steps of checking a printing object and judging whether the printing object is a preset first printing object; auditing the other party information, and judging whether the credit corresponding to the other party information is good or not; the second approval rule comprises any one or more of the following rules: verifying the signing main body, and judging whether the signing main body is a subordinate agency name; verifying the signed amount, and judging whether the signed amount is smaller than a second preset value; the printing object is checked, and whether the printing object is a preset second printing object is judged; and auditing the other party information, and judging whether the credit corresponding to the other party information is good or not.
Specifically, the contract approval apparatus in this embodiment further includes a display module, where the display module is configured to: and highlighting the keywords which are not approved according to a preset display mode so as to prompt.
Specifically, the contract approval apparatus in this embodiment further includes a position prompt module, where the position prompt module is specifically configured to: acquiring position information of the target keyword in the target contract; establishing a mapping relation between each target keyword and the position information to obtain a mapping relation table; and under the condition that the examination and approval of the target keywords are not passed, searching the mapping relation table based on the target keywords to obtain position information corresponding to the target keywords, and displaying the keywords at the corresponding positions in the target contract based on the position information and according to a preset display mode.
In this embodiment, the contract approval apparatus further includes a review module, and the review module is specifically configured to: determining the keyword type of the missing keywords to be acquired based on the preset keyword type and the keyword type of the target keywords; and acquiring the missing keywords based on the keyword types of the missing keywords, and examining and approving the target keywords and the missing keywords based on an examination and approval rule corresponding to the contract type.
The embodiment further includes a storage module, where the storage module is specifically configured to: determining a target storage location based on the format of the target contract for storage of the target contract.
According to the method and the device, a plurality of keywords are obtained by extracting the keywords from the contract file, the keywords are screened by using a K-means clustering algorithm to obtain a plurality of target keywords, and then the keywords are examined and approved by using the examination and approval rules which are preset and correspond to the contract types, so that the examination and approval rate of the contract can be improved, and the problem that a large amount of labor and time are consumed for contract examination and approval is solved.
Yet another embodiment of the present application provides a storage medium storing a computer program which, when executed by a processor, performs the method steps of:
step one, obtaining a target contract to be examined and approved;
secondly, extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
thirdly, screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
step four, determining the contract type of the target contract;
and fifthly, examining and approving the keywords at least based on an examination and approval rule corresponding to the contract type to obtain an examination and approval result of the target contract.
The specific implementation process of the above method steps can be referred to in the above embodiment of any contract approval method, and this embodiment is not repeated herein.
According to the method and the device, a plurality of keywords are obtained by extracting the keywords from the contract file, the keywords are screened by using a K-means clustering algorithm to obtain a plurality of target keywords, and then the keywords are examined and approved by using the examination and approval rules which are preset and correspond to the contract types, so that the examination and approval rate of the contract can be improved, and the problem that a large amount of labor and time are consumed for contract examination and approval is solved.
Yet another embodiment of the present application provides an electronic device, at least comprising a memory and a processor, the memory having a computer program stored thereon, the processor implementing the steps of the following method when executing the computer program on the memory:
step one, obtaining a target contract to be examined and approved;
secondly, extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
thirdly, screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
step four, determining the contract type of the target contract;
and fifthly, examining and approving the keywords at least based on an examination and approval rule corresponding to the contract type to obtain an examination and approval result of the target contract.
The specific implementation process of the above method steps can be referred to in the above embodiment of any contract approval method, and this embodiment is not repeated herein.
According to the method and the device, a plurality of keywords are obtained by extracting the keywords from the contract file, the keywords are screened by using a K-means clustering algorithm to obtain a plurality of target keywords, and then the keywords are examined and approved by using the examination and approval rules which are preset and correspond to the contract types, so that the examination and approval rate of the contract can be improved, and the problem that a large amount of labor and time are consumed for contract examination and approval is solved.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. A method of contract approval, comprising:
acquiring a target contract to be examined and approved;
extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
determining a contract type for the target contract;
and examining and approving each target keyword based on an examination and approval rule corresponding to the contract type to obtain an examination and approval result of the target contract.
2. The method of claim 1, wherein the method of training the target neural network language model comprises:
obtaining a plurality of corpus samples of contract types;
acquiring keywords corresponding to each of the speech samples to obtain a keyword set;
and training a neural network language model based on the corpus sample and the keyword set to obtain the target neural network language model.
3. The method of claim 1, wherein the filtering the keywords based on the K-means clustering algorithm specifically comprises:
calculating the distance between each keyword and a clustering center based on a K-means clustering algorithm;
and screening the keywords based on the distance between the keywords and the clustering center to obtain the target keywords.
4. The method of claim 1, wherein after approving each of the target keywords, the method further comprises:
and displaying the keywords which are not approved according to a preset display rule according to a preset display mode so as to prompt.
5. The method of claim 3, wherein after the screening for the target keyword, the method further comprises:
acquiring position information of the target keyword in the target contract;
establishing a mapping relation between each target keyword and the position information to obtain a mapping relation table;
under the condition that the target keyword is not approved, searching the mapping relation table based on the target keyword to obtain the position information corresponding to the target keyword,
and displaying the keywords at the corresponding positions in the target contract according to a preset display mode based on the position information.
6. The method of claim 3, wherein prior to approving each of the target keywords, the method further comprises:
determining the keyword type of the missing keywords to be acquired based on the preset keyword type and the keyword type of the target keywords;
and acquiring the missing keywords based on the keyword types of the missing keywords, and examining and approving the target keywords and the missing keywords based on an examination and approval rule corresponding to the contract type.
7. The method of claim 1, wherein after obtaining the target contract, the method further comprises:
determining a target storage location based on the format of the target contract for storage of the target contract.
8. A contract approval apparatus, comprising:
the acquisition module is used for acquiring a target contract to be examined and approved;
the extraction module is used for extracting keywords from the target contract based on a preset target neural network language model to obtain a plurality of keywords;
the screening module is used for screening the keywords based on a K-means clustering algorithm to obtain a plurality of target keywords;
a determining module for determining a contract type of the target contract;
and the approval module is used for approving the keywords at least based on the approval rule corresponding to the contract type to obtain the approval result of the target contract.
9. An electronic device, comprising at least a memory having a computer program stored thereon, and a processor, wherein the processor, when executing the computer program on the memory, implements the steps of the contract approval method of any one of claims 1-7.
10. A storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the steps of the contract approval method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110872691.4A CN113590823A (en) | 2021-07-30 | 2021-07-30 | Contract approval method and device, storage medium and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110872691.4A CN113590823A (en) | 2021-07-30 | 2021-07-30 | Contract approval method and device, storage medium and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113590823A true CN113590823A (en) | 2021-11-02 |
Family
ID=78252631
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110872691.4A Pending CN113590823A (en) | 2021-07-30 | 2021-07-30 | Contract approval method and device, storage medium and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113590823A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114445048A (en) * | 2022-01-30 | 2022-05-06 | 九恒星(武汉)信息技术有限公司 | Contract processing method, device, equipment and storage medium based on RPA |
| CN115497178A (en) * | 2022-11-21 | 2022-12-20 | 山东双仁信息技术有限公司 | Method and system for managing external vehicles in parking lot |
| CN117132244A (en) * | 2023-10-26 | 2023-11-28 | 国网浙江省电力有限公司 | Classification processing method, device and storage medium for intelligent compliance management system |
| CN117422428A (en) * | 2023-12-19 | 2024-01-19 | 尚恰实业有限公司 | Automatic examination and approval method and system for robot based on artificial intelligence |
| CN117436815A (en) * | 2023-11-17 | 2024-01-23 | 北京九思协同软件有限公司 | Flow intelligent approval method based on natural language big model |
| CN117495314A (en) * | 2024-01-02 | 2024-02-02 | 尚恰实业有限公司 | Automatic approval method and system based on machine learning |
-
2021
- 2021-07-30 CN CN202110872691.4A patent/CN113590823A/en active Pending
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114445048A (en) * | 2022-01-30 | 2022-05-06 | 九恒星(武汉)信息技术有限公司 | Contract processing method, device, equipment and storage medium based on RPA |
| CN115497178A (en) * | 2022-11-21 | 2022-12-20 | 山东双仁信息技术有限公司 | Method and system for managing external vehicles in parking lot |
| CN117132244A (en) * | 2023-10-26 | 2023-11-28 | 国网浙江省电力有限公司 | Classification processing method, device and storage medium for intelligent compliance management system |
| CN117132244B (en) * | 2023-10-26 | 2024-01-09 | 国网浙江省电力有限公司 | Classification processing method, device and storage medium for intelligent compliance management system |
| CN117436815A (en) * | 2023-11-17 | 2024-01-23 | 北京九思协同软件有限公司 | Flow intelligent approval method based on natural language big model |
| CN117436815B (en) * | 2023-11-17 | 2024-06-07 | 北京九思协同软件有限公司 | Flow intelligent approval method based on natural language big model |
| CN117422428A (en) * | 2023-12-19 | 2024-01-19 | 尚恰实业有限公司 | Automatic examination and approval method and system for robot based on artificial intelligence |
| CN117422428B (en) * | 2023-12-19 | 2024-03-08 | 尚恰实业有限公司 | Automatic examination and approval method and system for robot based on artificial intelligence |
| CN117495314A (en) * | 2024-01-02 | 2024-02-02 | 尚恰实业有限公司 | Automatic approval method and system based on machine learning |
| CN117495314B (en) * | 2024-01-02 | 2024-04-02 | 尚恰实业有限公司 | Automatic approval method and system based on machine learning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113590823A (en) | Contract approval method and device, storage medium and electronic equipment | |
| CN108876166A (en) | Financial risk authentication processing method, device, computer equipment and storage medium | |
| CN110765770A (en) | Automatic contract generation method and device | |
| CN110377631B (en) | Case information processing method, device, computer equipment and storage medium | |
| CN110362798B (en) | Method, apparatus, computer device and storage medium for judging information retrieval analysis | |
| CN110362542B (en) | Nuclear power station file encoding method and device, computer equipment and storage medium | |
| CN113627182B (en) | Data matching method, device, computer equipment and storage medium | |
| CN113256383B (en) | Recommendation method and device for insurance products, electronic equipment and storage medium | |
| KR102280490B1 (en) | Training data construction method for automatically generating training data for artificial intelligence model for counseling intention classification | |
| US10073891B2 (en) | Forensic system, forensic method, and forensic program | |
| CN113591480A (en) | Named entity identification method and device for power metering and computer equipment | |
| CN110674250A (en) | Text matching method, text matching device, computer system and readable storage medium | |
| CA3169288A1 (en) | Knowledge graph based reasoning recommendation system and method | |
| CN113269179B (en) | Data processing method, device, equipment and storage medium | |
| CN113297852B (en) | Medical entity word recognition method and device | |
| US11461616B2 (en) | Method and system for analyzing documents | |
| Amujala et al. | Digitization and data frames for card index records | |
| CN115293915A (en) | Service data verification method, device, equipment and storage medium | |
| CN113240325B (en) | Data processing method, device, equipment and storage medium | |
| CN115829722A (en) | Training method of credit risk scoring model and credit risk scoring method | |
| CN115760438A (en) | Digital dynamic underwriting system, method, equipment and storage medium | |
| CN113779998A (en) | Structured processing method, device and equipment of service information and storage medium | |
| CN114580398A (en) | Text information extraction model generation method, text information extraction method and device | |
| US20240202551A1 (en) | Visual Question Answering for Discrete Document Field Extraction | |
| CN116991983B (en) | Event extraction method and system for company information text |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |